A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data

Remote Sensing of Environment 102 (2006) 195 – 210 www.elsevier.com/locate/rse

A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data F. Casu a,b , M. Manzo a,c , R. Lanari a,⁎ a

Istituto per il Rilevamento Elettromagnetico dell'Ambiente, National Research Council, Via Diocleziano 328, 80124 Napoli, Italy b Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli studi di Cagliari, Piazza d’Armi, 09123 Cagliari, Italy c Dipartimento di Ingegneria e Fisica dell'Ambiente, Università degli Studi della Basilicata, Via Sauro 85, 85100 Potenza, Italy Received 2 November 2005; received in revised form 23 January 2006; accepted 26 January 2006

Abstract We investigate in this work the performance of the Small BAseline Subset (SBAS) approach that is a Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm allowing the generation of mean deformation velocity maps and displacement time series from a data set of subsequently acquired SAR images. In particular, we have carried out a quantitative assessment of the SBAS procedure performance by processing SAR data acquired by the European Remote Sensing Satellite (ERS) sensors and comparing the achieved results with geodetic measurements that are assumed as reference. The analysis has been focused on the Napoli bay (Italy) and Los Angeles (California) test areas where different deformation phenomena are present and, at the same time, a large amount of ERS SAR data is available as well as geometric leveling (in the Napoli zone) and continuous GPS (in the Los Angeles zone) measurements, to be used for our performance analysis. Moreover, due to the presence of large urbanized zones, the selected test sites are also characterized by extended, highly coherent areas in the DInSAR maps. The presented study shows that the SBAS technique provides an estimate of the mean deformation velocity with a standard deviation of about 1 mm/year for a typical ERS data set including between 40 and 60 images. Moreover, the single displacement measurements, computed with respect to a reference point of known motion, show a sub-centimetric accuracy with a standard deviation of about 5 mm, consistently in both the SAR/leveling and SAR/GPS comparisons; we also show that there is an increase of this standard deviation value as we move away from the reference SAR pixel, with an estimated spatial variation value of about 0.05 mm/km. © 2006 Elsevier Inc. All rights reserved. Keywords: Differential SAR interferometry; Surface deformations; Performance analysis; Quality assessment

1. Introduction Differential Synthetic Aperture Radar Interferometry (DInSAR) is a remote sensing technique that allows us to analyze deformation phenomena by exploiting the phase difference (usually referred to as interferogram) of SAR image pairs relevant to an area under study (Gabriel et al., 1989). In this context, several approaches aimed at following the temporal evolution of the detected displacements have been already presented, see Berardino et al. (2002), Crosetto et al. (2005), Ferretti et al. (2000), Hooper et al. (2004), Mora et al. (2003), Usai (2003), and Werner et al. (2003). Among these algorithms, ⁎ Corresponding author. Tel.: +39 081 570 7999; fax: +39 081 5705734. E-mail addresses: [email protected] (F. Casu), [email protected] (M. Manzo), [email protected] (R. Lanari). 0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2006.01.023

we consider the technique referred to as Small BAseline Subset (SBAS) approach, proposed by Berardino et al. (2002), whose capability to generate mean deformation velocity maps and displacement time series from the European Remote Sensing Satellite (ERS) SAR data has been already exploited in different works (Borgia et al., 2005; Lanari et al., 2002, 2004a,b; Lundgren et al., 2004). In these studies some comparisons between the achieved DInSAR products and geodetic measurements have been shown in order to confirm the presented results, but no extensive analysis on the quality of the DInSAR measurements has been carried out. Accordingly, the key idea of this work is to provide a quantitative assessment of the SBAS algorithm performance; in particular, we concentrate on the basic SBAS technique that has been originally developed to investigate large spatial scale displacements by exploiting low-pass filtered (multilook) DInSAR interferograms (Rosen et al., 2000).

196

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

Our analysis is focused on two test areas where different deformation phenomena are present and, at the same time, a large amount of SAR data is available as well as ground measurements to be used for an extensive SAR/geodetic data comparison. Note also that, due to the presence of large urbanized zones, the selected test sites are also characterized by extended, highly coherent areas in the DInSAR maps. We stress that the goal of the presented study is not the investigation of the detected deformation processes, which have been already analyzed in previous works exploiting the DInSAR technology. On the contrary, we benefit from the knowledge of these phenomena and of the availability of a large data set of SAR and geodetic measurements, the latter assumed as reference, for carrying out the assessment of the SBAS procedure performance. In particular, the first test site is located in the Napoli bay (Italy) area which includes the highly urbanized zone of the city of Napoli and three active volcanoes (the Campi Flegrei caldera, the Somma–Vesuvio volcanic complex and the Ischia island) whose overall deformations have been already studied via DInSAR techniques, see Avallone et al. (1999), Beauducel et al. (2004), Borgia et al. (2005), Lanari et al. (2002, 2004a,c), Lundgren et al. (2001), and Tesauro et al. (2000). In this area a very large spirit leveling network is present, including several hundreds of benchmarks with repeat measurements that are systematically carried out (INGV-OV, 2001, 2002, 2003). Moreover, a huge SAR data set relevant to ascending and descending tracks of the ERS-1 and ERS-2 sensors, spanning the time interval from 1992 until 2003, is available to us. The availability of these SAR data permits the analysis of the temporal behavior of the detected deformations and, at the same time, the discrimination of vertical and east–west displacement components. The former has been compared with the measurements available from the leveling campaigns in areas where both SAR and geodetic data were available. The second test area is the Los Angeles (California) metropolitan zone which is a tectonically active region with surface deformations that are a combination of natural and anthropogenic signals. Also in this case a large number of ERS SAR data are available, in particular acquired from descending orbits, that have been already exploited to investigate the ongoing phenomena, see Argus et al. (2005), Bawden et al. (2001), Colesanti et al. (2003), Lanari et al. (2004b), and Watson et al. (2002). Moreover, a very large amount of geodetic measurements have been recorded through the Southern California Integrated GPS Network (SCIGN), see Hudnut et al. (2001). Accordingly, a detailed comparison between the DInSAR and the GPS measurements has been carried out in this case. As a result of the overall DInSAR/geodetic measurements comparison, a quantitative assessment of the SBAS procedure performance for surface deformations retrieval has been finally provided. The paper is organized as follows: a short overview on the basic rationale of the SBAS algorithm is presented first. The following sections are focused on the comparison between the achieved DInSAR results and the leveling and GPS measurements in the Napoli bay and Los Angeles areas, respectively.

The last section is dedicated to the conclusions, summarizing the main findings of the SBAS algorithm performance assessment. 2. Rationale of the SBAS algorithm The Small BAseline Subset technique is a DInSAR approach that relies on the use of a large number of SAR acquisitions and implements an easy combination of a properly chosen set of multilook DInSAR interferograms computed from these data, finally leading to the generation of mean deformation velocity maps and displacement time series. A detailed discussion on the basic SBAS approach is clearly outside the scope of this work; accordingly, we highlight in this section what are the key issues of the algorithm and refer to Berardino et al. (2002) for a more detailed analysis. Let us start our discussion by considering a set of N + 1 SAR images relative to the same area, acquired at the ordered times (t0, …, tN); we also assume that they are co-registered with respect to an image referred to as master one that allows us to identify a common (reference) spatial grid. The starting point of the SBAS technique is represented by the generation of a number, say M, of multilook DInSAR interferograms that involve the previously mentioned set of N + 1 SAR acquisitions. Note also that each of these interferograms is calibrated with respect to a single pixel located in an area that can be assumed stable or, at least, with a known deformation behavior; this point is often referred to as reference SAR pixel. Let us now consider a generic pixel of azimuth and range coordinates (x, r); the expression of the generic j-th interferogram computed from the SAR acquisitions at times tB and tA, according to Berardino et al. (2002), will be the following: d/j ðx; rÞ

¼ /ðtB ; x; rÞ−/ðtA ; x; rÞ 4p ð x; rÞ c ½d ðtB ; x; rÞ−d ðtA ; x; rÞ þ D/topo j k þ D/atm j ðtB ; tA ; x; r Þ þ Dnj ð x; r Þ

ð1Þ

wherein j ∈ (1, …, M), λ is the transmitted signal central wavelength, ϕ (tB, x, r) and ϕ(tA, x, r) represent the phases of the two images involved in the interferogram generation and d (tB, x, r) and d (tA, x, r) are the radar line of sight (LOS) projections of the cumulative deformations at times tB and tA, with respect to the instant t0 assumed as a reference and implying ϕ (t0, x, r) = 0, ∀ (x, r). Moreover, for what concerns the right hand side of the last identity in Eq. (1), the second term Δϕjtopo (x, r) accounts for possible topographic artifacts that can be present in the Digital Elevation Model (DEM) used for the interferogram generation. Finally, the term Δϕjatm (tB, tA, x, r) accomplishes for possible inhomogeneities between the two acquisitions, usually referred to as atmospheric phase artifacts (Goldstein, 1995), while the last factor Δnj (x, r) accounts for the noise effects referred to as decorrelation phenomena (Zebker & Villasenor, 1992). The main tasks of the SBAS procedure can be synthesized as follows: first of all, we have to identify which are the coherent pixels of our interferograms, i.e., those pixels characterized by small values of the factor Δnj (x, r), see Eq. (1), ∀j = 1, …, M. Subsequently, the algorithm must provide an estimate of the

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

deformation time series d (ti, x, r), ∀ti = t0, …, tN, for each coherent pixel. In order to achieve this task, we have to properly combine the information relevant to all the interferograms, i.e., δϕj (x, r), ∀j = 1, …, M, and detect and cancel out the topographic Δϕjtopo (x, r) and atmospheric Δϕjatm (tB, tA, x, r) signal components highlighted in Eq. (1). The key steps involved in the displacement time series retrieval, implemented via the SBAS algorithm, are the following: • the data pairs used to generate the multilook DInSAR interferograms are properly chosen with the key objective to mitigate the decorrelation phenomena (Zebker & Villasenor, 1992). In particular, this data pairs selection involves the introduction of constraints on the allowed maximum spatial and temporal separation (baseline) between the orbits relevant to the interferometric SAR image couples and has a main goal of maximizing the number of coherent pixels in our multilook interferograms; • a retrieval step (usually referred to as phase unwrapping operation) of the original (unwrapped) phase δϕj (x, r), see Eq. (1), from the modulo-2π restricted (wrapped) signal directly computed from the generated multilook interferograms, is carried out. This operation is implemented via the procedure described by Costantini and Rosen (1999). It allows us to process data available on a sparse grid that in our case is relevant to the pixels remaining coherent in the investigated interferograms. The basic algorithm of Costantini and Rosen (1999) is also integrated with a region growing procedure allowing us to improve the algorithm performances in areas with relatively low coherence; • the Singular Value Decomposition (SVD) method is applied to “combine” the unwrapped DInSAR interferograms. In particular, the SBAS approach implies the solution, on a (coherent) pixel by pixel basis, of the linear system of equations based on Eq. (1), ∀j = 1, …, M, in order to get an estimate of the deformation time series. However, the previously mentioned baseline constraints in the data pairs selection may have as a consequence that the SAR data involved in the interferograms generation belong to “independent subset”, thus causing our system of equations to have infinite solutions. Accordingly, the application of the SVD method within the SBAS technique allows us to regularize the problem and to generate the minimum norm

197

Least Square (LS) solution of our system of equations that, as shown by Lanari et al. (2004d), generally guarantees a physically sound solution; in any case, we tend to minimize the number of data subsets. We further remark that, as an additional result of our system of equations solution, we also get an estimate of possible topographic artifacts (also referred to as residual topography) that can be present in the DEM used for the DInSAR interferograms generation; • as a final step of the SBAS procedure, a space–time filtering operation is carried out in order to estimate and subsequently remove possible artifacts due to atmospheric inhomogeneities between the acquisition pairs. This operation is based on the observation that the atmospheric signal phase component is highly correlated in space but poorly in time, see Ferretti et al. (2000). Accordingly, the undesired atmospheric phase signal is estimated from the time series computed via the SVD technique through the cascade of a lowpass filtering step in the two-dimensional spatial domain followed by a temporal highpass filtering operation. Moreover, this operation also allows us to detect possible orbital fringes caused by inaccuracies in the SAR sensors orbit information (Lanari et al., 2004b). Indeed, such errors are also typically not correlated in time but strongly correlated in space and they are often well approximated by spatial ramps usually referred to as orbital ramps. Based on these considerations, we perform in our approach an estimate of these orbital patterns by searching for the best-fit ramp to the temporal high-pass/ spatially low-pass time series signal component; following this step, we remove the detected ramps from each differential interferograms. Accordingly, we do not use additional information obtained from a set of ground control points to get rid of these orbital errors. The removal of the estimated atmospheric artifacts and orbital ramps finally leads to the generation of the required deformation time series. In summary the SBAS technique, whose block diagram is sketched in Fig. 1, allows us to satisfy two key requirements: to follow the temporal evolution of the detected displacements by using nearly all the available SAR acquisitions and to preserve the capabilities of the system to provide spatially dense deformation maps, which is a key issue of conventional DInSAR interferometry.

Fig. 1. SBAS algorithm block diagram.

198

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

Fig. 2. SAR amplitude image of the investigated portion of the Napoli bay (Italy) area. The benchmarks of the existing leveling network have been identified by black squares; moreover, the main locations within the area have been also highlighted. Note that the two benchmarks labeled LNA001 and LVE078 represent the reference points for the Campi Flegrei and the Vesuvio leveling network portions, respectively. The inset in upper right corner shows the location of the study area.

As final remark we want to stress that the above described SBAS approach has been recently extended to analyze also full resolution (single-look) DInSAR interferograms (Lanari et al., 2004d) and for the generation of multisensor, for instance ERS and ENVISAT, displacement time series, see Pepe et al. (2005). However, the following analysis will be focused on the basic SBAS procedure applied to multilook interferograms generated from SAR data acquired by the ERS SAR sensors. 3. Comparison between SBAS-DInSAR and spirit leveling measurements: the Napoli bay case study The following section is dedicated to present the results of the comparison between the deformation measurements retrieved via the SBAS technique and those available from geometric leveling campaigns. In particular, our analysis is focused on the Neapolitan volcanic area that includes three active volcanoes: the Campi Flegrei caldera, the Ischia island and the Somma–Vesuvio complex, all characterized by a high risk degree because 2.5 million people live under their threats; this scenario clearly explains the necessity of providing these areas with monitoring networks in order to reveal the resumption of volcanic phenomena. In our study we focused on the portion of the Napoli bay area extending from the NW border of the Campi Flegrei caldera up to the SE margin of the Vesuvio volcano, thus including the entire Napoli city area, see Fig. 2. In this zone one of the most extended geometric leveling networks of the world, with about 600 benchmarks and more than 350 km of linear extension, is present (INGV-OV, 2001, 2002, 2003). This network, see Fig. 2, managed by the Osservatorio Vesuviano (OV) belonging to the Italian National Institute of Geophysics and Volcanology (INGV), is a very important element of the surveillance system of this area because it allows to achieve measurements of the vertical component of the deformations with a high accuracy

(INGV-OV, 2003). In particular, the spirit leveling network component relevant to the Campi Flegrei area includes 300 benchmarks, extending for about 120 linear km, organized into 11 linked loops; the reference benchmark is located in the Napoli city (more precisely in the Mergellina Harbor zone, marked by LNA001 in Fig. 2) and the whole network is periodically measured. The portion of the network dedicated to monitor the Somma–Vesuvio volcano complex consists of about 290 benchmarks, extending for 220 linear km, organized in 15 closed circuits partially overlapping with the Campi Flegrei network component. Also the whole vesuvian leveling network is periodically measured and all the benchmarks are typically referred to the one located in the Sorrento Peninsula (benchmark LVE078, see Fig. 2); however, in our study we have re-referred all the available measurements to the same benchmark LNA001, located in the Napoli city, in order to have a single reference point consistent with what occurs for the DInSAR results. As far as the SAR measurements are concerned, we used in our study 116 SAR data acquired by the European Space Agency (ESA) ERS-1/2 sensors. In particular, 58 acquisitions are relevant to ascending passes (track: 129, frame: 809) spanning the January 1993–May 2003 time interval; the remaining 58 data have been acquired from descending orbits (track: 36, frame: 2781) between June 1992 and October 2002. Each interferometric SAR image pair has been chosen with a perpendicular baseline value smaller than 300 m and with a maximum time interval of 4 years; precise satellite orbital information and a Shuttle Radar Topography Mission (SRTM) DEM (Rosen et al., 2001) of the study area have also been used. Starting from the available SAR data set,1 we have computed 133 interferograms 1 Note that the investigated SAR data are the same exploited by Borgia et al. (2005) for studying the Vesuvio volcano but in this work the processed area extends to the entire Napoli bay zone.

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

199

Fig. 3. Mean deformation velocity maps: a) LOS deformation map computed in coherent areas of the ascending ERS data set, spanning the January 1993–May 2003 time interval, and superimposed on the SAR amplitude image of the Napoli bay area; b) LOS deformation map computed from the descending ERS data acquired in the time period between June 1992 and October 2002; c) east–west deformation component computed from the ascending and descending velocity patterns shown in (a) and (b), respectively, for those pixels which are common to both maps; d) vertical deformation component map. Note that the red color corresponds in (a) and (b) to an increase of the sensor–pixel distance, in (c) to a displacement toward west, and in (d) to a subsidence effect. Moreover, the black square in (a)–(d) identifies the

200

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

from the acquisitions relevant to the ascending orbits, while 148 interferograms have been produced from the descending orbits data. All the DInSAR products have been obtained following a

complex multilook operation (Rosen et al., 2000) with 4 looks in the range direction and 20 looks in the azimuth one, with a resulting pixel dimension of the order of 100 × 100 m.

0 -2 -4 -6 0

σv = 1.3mm/year 5 10 Distance [km]

15

0.5

d

0.0 -0.5 -1.0 -0.5

σv = 0.7mm/year 0.0

0.5

1.0 1.5 Distance [km]

2.0

0.5

2.5

f

0.0 -0.5 -1.0 -1.5 -2

σv = 0.7mm/year 0

2

4 6 Distance [km]

8

10

Mean velocity [cm/year]

b

Mean velocity [cm/year]

2

Mean velocity [cm/year]

Mean velocity [cm/year]

Mean velocity [cm/year]

Mean velocity [cm/year]

a 2

c

0 -2 -4

σv = 0.8mm/year

-6 0

5 10 Distance [km]

15

0.5

e

0.0 -0.5 σv = 1.0mm/year

-1.0 0

10

20 Distance [km]

30

0.5

g

0.0 -0.5 -1.0 -1.5 -2

σv = 0.5mm/year 0

2

4 6 Distance [km]

8

10

Fig. 4. SAR/leveling vertical mean deformation velocities comparison: a) SAR amplitude image of the investigated zone with superimposed a selection of leveling network lines identified by different colored squares, for which the geodetic measurements are available to us. The location of the reference leveling benchmark LNA001 is highlighted as well as the beginning (square) and the end (triangle) of each line; b) plot of the mean vertical deformation velocity computed from the SAR data (red triangles) compared to the corresponding one relevant to the leveling measurements (black stars) along the line identified in (a) by the green squares; c) plot along the line identified in (a) by the red squares; d) plot along the line identified in (a) by the cyan squares; e) plot along the line identified in (a) by the blue squares; f) plot along the line identified in (a) by the yellow squares; g) plot along the line identified in (a) by the violet squares. The standard deviation value of the difference between SAR and leveling vertical velocities has been reported in each plot of (b)–(g).

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

201

Table 1 Results of the comparison between SAR and leveling mean deformation velocities relevant to the Napoli bay area Lines identified by colors in Fig. 4a

Leveling benchmarks

DInSAR measurements E–W mean deformation velocity [cm/year]

Leveling measurements

Vertical mean deformation velocity [cm/year]

Vertical mean deformation velocity [cm/year]

Difference between SAR and leveling vertical mean deformation velocities [cm/year]

Green line

LCF/002 LCF/10 LCF/012A LCF/016 LCF/017A LCF/020 LCF/022 LCF/25A LCF/030 LCF/035 LCF/045 LCF/051 LCF/166 LCF/170 LCF/172

0.14 −0.53 −0.67 −1.27 −1.41 −1.22 −1.03 0.13 0.08 0.97 0.74 0.43 0.41 0.37 0.27

− 0.16 − 0.21 − 0.28 − 1.10 − 1.43 − 2.58 − 2.82 − 2.85 − 2.74 − 1.29 − 0.24 − 0.40 − 0.17 − 0.08 − 0.08

0.01 − 0.22 − 0.31 − 1.10 − 1.43 − 2.41 − 2.79 − 2.91 − 2.69 − 1.40 −0.24 − 0.29 − 0.22 − 0.44 − 0.10

− 0.17 0.01 0.03 0.00 0.00 − 0.17 − 0.03 0.06 − 0.05 0.11 0.00 − 0.11 0.05 0.36 0.02

Red line

LCF/073 LCF/063 LCF/086 LCF/087 LCF/088 LCF/089 LCF/090 LCF/091 LCF/092 LCF/093 LCF/100 LCF/101 LCF/102 LCF/106 LCF/107 LCF/114A LCF/116 LCF/118

−0.95 −0.25 0.14 0.23 0.15 0.07 0.07 0.11 0.01 0.08 −0.26 −0.19 −0.46 −0.38 −0.32 −0.57 −0.82 −0.82

− 0.71 − 2.51 − 0.69 − 0.59 − 0.45 − 0.36 − 0.24 − 0.16 − 0.08 − 0.10 − 0.15 − 0.14 − 0.28 − 0.12 − 0.15 − 0.43 − 0.65 − 0.61

− 0.77 − 2.66 − 0.91 − 0.76 − 0.62 − 0.49 − 0.40 − 0.31 − 0.13 − 0.08 − 0.33 − 0.29 − 0.27 − 0.21 − 0.30 − 0.46 − 0.90 − 0.63

0.06 0.15 0.22 0.17 0.17 0.13 0.16 0.15 0.05 − 0.02 0.18 0.15 − 0.01 0.09 0.15 0.03 0.25 0.02

Cyan line

LCF/233 LCF/234 LCF/235 LCF/236 LCF/237

−0.07 −0.16 −0.07 0.00 −0.08

− 0.16 − 0.31 − 0.17 − 0.55 − 0.26

− 0.26 − 0.34 − 0.20 − 0.55 − 0.17

0.10 0.03 0.03 0.00 − 0.09

Blue Line

LVE059 LVE64B18P LVE64B19 LVE060 LVE061 LVE062 LVE063 LVE10L LVE083/068 LVE083/067C LVE143P LVE083/066 LVE083/065 LVE083/064P LVE083/064 LVE083/063 LVE083/062 LVE083/061C LVE56L

0.03 −0.04 −0.06 0.04 −0.08 0.21 −0.11 0.01 0.04 −0.05 −0.10 0.00 0.10 0.21 0.10 0.19 0.31 0.25 0.30

0.04 0.04 0.06 0.04 0.08 0.02 0.06 − 0.03 − 0.04 0.02 0.06 0.04 0.03 0.03 − 0.01 0.04 − 0.02 0.01 − 0.01

0.00 0.00 0.00 0.02 0.06 0.01 − 0.01 0.00 − 0.03 − 0.05 − 0.03 − 0.17 − 0.22 − 0.17 − 0.15 − 0.18 − 0.19 − 0.19 − 0.21

0.04 0.04 0.06 0.02 0.02 0.01 0.07 − 0.03 − 0.01 0.07 0.09 0.21 0.25 0.20 0.14 0.22 0.17 0.20 0.20 (continued on next page)

202

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

Table 1 (continued) Lines identified by colors in Fig. 4a

Leveling benchmarks

DInSAR measurements E–W mean deformation velocity [cm/year]

Leveling measurements

Vertical mean deformation velocity [cm/year]

Vertical mean deformation velocity [cm/year]

Difference between SAR and leveling vertical mean deformation velocities [cm/year]

Blue line

LVE57L LVE083/060C LVE083/059 LVE083/058 LVE083/057N LVE083/057 LVE083/055 LVE083/054C LVE064 LVE083/052S LVE065 LVE066 LVE067 LVE068 LVE069P LVE070P LVE071 LVE114 LVE113

0.35 0.30 0.16 0.26 0.34 0.39 0.35 0.36 0.39 0.39 0.55 0.45 0.37 0.47 0.39 0.40 0.44 0.53 0.41

−0.04 0.03 −0.03 0.02 −0.01 −0.05 −0.05 −0.01 −0.08 −0.04 −0.05 0.07 0.02 −0.09 −0.12 −0.17 −0.14 −0.16 −0.06

− 0.17 − 0.18 − 0.16 − 0.23 − 0.20 − 0.19 − 0.12 − 0.16 − 0.13 0.13 − 0.07 0.03 − 0.13 0.03 − 0.19 − 0.19 − 0.28 − 0.26 − 0.20

0.13 0.21 0.13 0.25 0.19 0.14 0.07 0.15 0.05 − 0.17 0.02 0.04 0.15 − 0.12 0.07 0.02 0.14 0.10 0.14

Yellow line

LVE079 LVE080 LVE20L LVE21L LVE081 LVE082 LVE083 LVE034 LVE33P LVE027LP LVE024 LVE010

0.24 0.11 0.24 0.21 0.22 0.13 0.27 0.23 0.14 0.31 0.27 0.15

−0.03 0.05 −0.02 −0.05 −0.02 0.02 −0.04 −0.11 −0.09 −0.17 −0.18 −0.28

− 0.08 − 0.06 − 0.08 − 0.02 − 0.04 − 0.03 − 0.16 − 0.11 − 0.08 − 0.12 − 0.09 − 0.16

0.05 0.11 0.06 − 0.03 0.02 0.05 0.12 0.00 − 0.01 − 0.05 − 0.09 − 0.12

Violet line

LVE053 LVE054 LVE055 LVE056 LVE057

0.46 0.28 0.45 0.38 0.43

−0.03 −0.10 −0.05 −0.11 −0.09

− 0.17 − 0.12 − 0.14 − 0.17 − 0.14

0.14 0.02 0.09 0.06 0.05

As a first result achieved by applying the SBAS approach, we present in Fig. 3a and b the estimated radar line of sight mean deformation velocities relevant to the ascending and descending SAR acquisitions, respectively. They have been computed with respect to a reference pixel located in the Napoli Harbor zone, corresponding to the reference leveling benchmark LNA001 of Fig. 2. Note also that the DInSAR products shown in Fig. 3a and b have been geocoded and superimposed on a SAR amplitude image of the zone; moreover, the noisy areas with low accuracy measurements have been excluded. By considering Fig. 3a and b we remark that reliable information is available only on urbanized zones and rocky areas. Regarding the detected main deformation patterns, we observe that significant LOS displacements are visible on several coherent areas which can be easily identified: the Campi Flegrei caldera, on the left hand side, various deforming zones within the city of Napoli approximately located in the image centre and other displacements occurring on the top and around the base of

the Vesuvio as well as in the eastern and south-eastern sectors of the image. Moreover, we also stress that the availability of both ascending and descending data allows us to detect not only the LOS ground deformations but also to discriminate vertical and east–west displacement components, as already shown by Borgia et al. (2005) and Lundgren et al. (2004). In particular, the difference between the mean surface velocity maps computed from the ascending and descending orbits (see Fig. 3a and b, respectively) on pixels that are common to both maps, allows us to get an estimate of the surface deformation velocities in the east–west direction, see Fig. 3c. In addition, the sum of the ascending and descending deformation patterns allows us to get a picture that is mostly vertical motion, see Fig. 3d. The availability of the estimated vertical deformation component shown in Fig. 3d represents a key element of our study because it allows us to carry out a quantitative

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

203

Fig. 5. Locations of the benchmarks of the leveling network (black and white squares) characterized by a dominantly vertical displacement, superimposed on the mean vertical deformation velocity map of Fig. 3d. White squares mark the selected benchmarks relevant to the following plots shown in Fig. 6.

comparison between the DInSAR results and the vertical displacement measurements available from the previously described leveling network. The basic idea, in the following analysis, is to benefit from the space–time information available from the SAR data and, for coherent SAR pixels common to both ascending and descending maps and located in correspondence of leveling benchmarks, to carry out the following two operations: first of all comparing the mean vertical displacement velocities estimated from the SAR and the geodetic data; subsequently, in areas characterized by a nearly vertical deformation only, comparing the time series achieved from the SAR data relevant to the ascending and descending orbits with those available from the leveling measurements, the latter projected in the radar LOS2 to be consistent with the DInSAR data. In particular, our comparison is focused on the benchmarks of the leveling network identified by the six lines shown in Fig. 4a. These lines, for which leveling measurements spanning the 1990–2003 time interval have been considered because of their temporal overlap with the overall available SAR data set, represent the part of the whole leveling network shown in Fig. 2 for which measurements were made available to us. As concerns the first issue of our comparison, the achieved results are presented in the plots of Fig. 4b-g for each leveling line. They clearly show the good agreement between the two mean vertical velocity measurements (SAR: red triangles, leveling: black stars). Moreover, a detailed list of the estimated DInSAR E–W and vertical mean velocity displacements is provided in Table 1 (see the third and the fourth columns from the left, respectively) for all coherent pixels associated to the leveling benchmarks. In addition, in Table 1 the vertical deformation velocity computed from the leveling measure2

A 23° mean incidence angle is assumed in this case.

ments within the interval common to the SAR data and the corresponding difference between the SAR and the leveling data (see the fifth and the sixth columns from the left, respectively) is also reported. Based on these last results shown in the right hand side column of Table 1, we have computed the standard deviation value, say σv, of the difference between SAR and leveling vertical velocities; we obtained σv = 1.0 mm/year, corresponding in LOS to about 0.9 mm/year. Note also that about 76% and 96% of the differences between SAR and leveling vertical velocities are included within the (− σv, σv) and (− 2σv, 2σv) intervals, respectively. For what concerns the second issue of our comparison, we have identified all the leveling benchmarks corresponding to coherent pixels in both ascending and descending maps, where the amplitude of the estimated horizontal mean deformation velocity (see the third column from the left of Table 1) is significantly small and in particular, smaller than a selected threshold that in our case is assumed of 2.5 mm/year; the remaining benchmarks are not considered in the following analysis. This implies that, due to the deformation phenomena affecting the Campi Flegrei caldera (Lundgren et al., 2001) and the Vesuvio volcano flank (Borgia et al., 2005), see Fig. 3c, several benchmarks characterized by displacements with significant horizontal components have been discarded. The leveling benchmarks identified after this selection process have been superimposed to the vertical mean deformation velocity map of Fig. 3d, as shown in Fig. 5. Note that for these pixels we can now compare the temporal evolution of the detected deformations from both the ascending and descending orbits with the leveling measurements projected in the radar line of sight. In this case, we have provided first a qualitative comparison on a selection of twelve benchmarks with different spatial locations and features

204

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

LCF/030

a

-10 -20 -30

σd

LA

1990

= 9.7mm ; σd

LD

1992

= 10.9mm

1996 1998 Time [years]

1994

2000

2002

Displacement [cm]

Displacement [cm]

LCF/25A 0

-5 -10

σd

LA

1990

= 5.3mm ; σd

1992

LD

= 6.3mm

1996 1998 Time [years]

1994

2000

2002

Displacement [cm]

Displacement [cm]

LCF/087 0

-10 -20 -30

σd

LA

1990

2004

c

b

0

2004

= 8.3mm ; σd

LD

1992

Displacement [cm]

Displacement [cm]

e

0 -2

2000

2002

2004

0 -2 -4 σd = 3.5mm ; σd = 4.2mm LA LD -6 1990 1992 1994 1996 1998 Time [years]

2002

Displacement [cm]

Displacement [cm]

2004

f

2 0 -2 -4 σ dLA = 3.7mm ; σdLD = 4.2mm -6 1998 2000 1994 1996 Time [years]

0

1992

1994

= 5.4mm

1996 1998 Time [years]

2000

2002

2004

0 -2 -4

σd

LA

= 2.1mm ; σd

1992

LD

1994

= 3.7mm

1996 1998 Time [years]

2000

2002

2004

4

j

2 0 -2

σd = 3.2mm ; σd = 5.4mm LA LD -4 1990 1992 1994 1996 1998 Time [years]

2000

2002

2004

LVE010

k

2 0 -2 -4

2000

2002

2004

Displacement [cm]

Displacement [cm]

2004

h

2

LVE034 4

σd = 4.7mm ; σd = 5.1mm LA LD -6 1990 1992 1994 1996 1998 Time [years]

2002

4

1990

Displacement [cm]

Displacement [cm]

i

-4 1990

2004

LVE083/067C

2

LD

2002

4

LVE083/063

= 6.2mm ; σd

2000

LVE061

g

4

LA

2004

d

2

LCF/236 4 2 0 -2 -4 -6 -8 σd = 3.2mm ; σd = 4.0mm LA LD -10 1994 1996 1998 2000 Time [years]

σd

2002

LCF/234

2

-2

2000

LCF/092

4

LCF/101 4

-4 σ dLA = 7.8mm ; σdLD = 3.3mm -6 1990 1992 1994 1996 1998 Time [years]

= 8.5mm

1996 1998 Time [years]

1994

4

l

2 0 -2 -4

σd = 9.0mm ; σd = 7.0mm LA LD -6 1990 1992 1994 1996 1998 Time [years]

2000

2002

2004

Fig. 6. Comparison between the DInSAR LOS deformation time series (ERS ascending data: blue triangles, ERS descending data: red triangles) and the corresponding leveling measurements projected on the radar LOS (black stars) for the pixels labeled in Fig. 5 as LCF/25A (a), LCF/030 (b), LCF/087 (c), LCF/092 (d), LCF/101 (e), LCF/234 (f), LCF/236 (g), LVE061 (h), LVE083/063 (i), LVE083/067C (j), LVE034 (k) and LVE010 (l), respectively. The standard deviation value of the differences between the SAR (ascending and descending) and LOS-projected leveling measurements has been reported in the plots (a)–(l).

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

in their time series; they are labeled in Fig. 5 as LCF/25A, LCF/030, LCF/087, LCF/092, LCF/101, LCF/234, LCF/236, LVE061, LVE083/063, LVE083/067C, LVE034 and LVE010, respectively. For each of these benchmarks we have superimposed the line of sight projected leveling measurements and the deformation time series, computed from the ascending and descending data. These comparisons are shown in Fig. 6. By observing the presented plots, the strong similarity between the temporal evolution of the ascending (blue triangles) and descending (red triangles) SAR measurements, supporting the previous assumption of a dominantly vertical displacement, as well as their agreement with the LOS-projected leveling (black stars) deformation time series, is clear. Moreover, we have also carried out a quantitative assessment of the DInSAR time series quality. To achieve this result, we have systematically compared all the DInSAR and LOS-projected leveling time series, the latter interpolated via a linear regression within the interval common to the SAR data, for all the benchmarks identified in Fig. 5 by the black and white squares. In particular, we have computed the standard deviation value of the difference between the two time series; the obtained results are shown in Table 2. Note that if we compute the average value of the standard deviations relevant to the differences between SAR and LOS-projected leveling measurements, we obtain that they are consistent between ascending and descending products and correspond to rdLA ¼ 4:6 mm for the ascending and rdLD ¼ 4:8 mm for the descending data, respectively. Accordingly, we may finally assume as conclusive value for the standard deviation of the difference between SAR and leveling data the factor rdL ¼ 4:7 mm, obtained by further averaging the standard deviation values of the ascending and descending results. Note also that 60% and 96% of the differences between ascending SAR and leveling data are included within the ð−rdLA ; rdLA Þ and ð−2rdLA ; 2rdLA Þ intervals, respectively; the homologous values for the descending products are nearly identical, corresponding to 60% and 98%, respectively. We want to finally stress that, in our deformation time series analysis, by discarding points where significant horizontal deformation components have been detected (in particular, with an amplitude greater than 2.5 mm/year), we are not eliminating “bad pixels”, i.e., those affected by significant errors. To verify our statement, we have shown that the discarded pixels have essentially the same accuracy, for what concerns the retrieved deformation measurements, of the remaining investigated points; of course, this assessment could be done for the measured mean deformation velocity analysis, only. To achieve this task, we have divided the overall investigated coherent pixels (93 in total) in two portions; the first one, involving 44 pixels, is relevant to the points characterized by an estimated horizontal mean deformation velocity amplitude smaller than 2.5 mm/year. The second portion corresponds to the remaining 49 pixels characterized by an estimated horizontal mean deformation velocity amplitude greater than 2.5 mm/year. For these two classes of data, we have computed the standard deviation of the difference between the computed DInSAR and leveling mean vertical deformation velocities. In the former case

205

Table 2 Results of the comparison between SAR and LOS-projected leveling deformation time series relevant to the Napoli bay area Leveling benchmarks

LCF/002 LCF/25A LCF/030 LCF/063 LCF/086 LCF/087 LCF/088 LCF/089 LCF/090 LCF/091 LCF/092 LCF/093 LCF/101 LCF/233 LCF/234 LCF/235 LCF/236 LCF/237 LVE059 LVE64B18P LVE64B19 LVE060 LVE061 LVE062 LVE063 LVE10L LVE083/068 LVE083/067C LVE143P LVE083/066 LVE083/065 LVE083/064P LVE083/064 LVE083/063 LVE083/059 LVE079 LVE080 LVE20L LVE21L LVE081 LVE082 LVE034 LVE33P LVE010

Standard deviation of the difference between SAR and LOS-projected leveling measurements [cm] Ascending

Descending

0.42 0.97 0.83 1.09 0.64 0.53 0.54 0.49 0.52 0.39 0.35 0.46 0.78 0.38 0.37 0.32 0.32 0.35 0.31 0.30 0.25 0.29 0.21 0.33 0.42 0.35 0.27 0.32 0.39 0.59 0.69 0.51 0.52 0.62 0.56 0.36 0.40 0.31 0.32 0.30 0.33 0.47 0.48 0.90

0.31 1.09 0.85 0.96 0.78 0.63 0.57 0.50 0.52 0.65 0.42 0.45 0.33 0.36 0.42 0.35 0.40 0.43 0.23 0.26 0.51 0.38 0.37 0.33 0.36 0.42 0.39 0.54 0.31 0.47 0.59 0.59 0.48 0.54 0.64 0.46 0.44 0.39 0.43 0.44 0.41 0.51 0.49 0.70

we obtained a standard deviation σv = 0.92 mm/year, while in the latter we got σv = 1.09 mm/year. These two results are consistent and in very good agreement with the already computed overall standard deviation value. Accordingly, by removing from our deformation time series analysis the pixels characterized by an horizontal velocity amplitude greater than 2.5 mm/year, we may expect that we are not excluding points with large errors (the errors are of the same order of those affecting pixels with small horizontal deformation components), but simply pixels that cannot be really investigated. Indeed, due to the horizontal deformation component, these points cannot show consistent results among the leveling and the ascending and descending DInSAR time series.

206

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

4. Comparison between SBAS-DInSAR and GPS measurements: the Los Angeles metropolitan area case study The results presented in the following section are focused on the comparison between the SBAS-DInSAR results and the measurements available from a continuous GPS network. In particular, the investigated test site is represented by the Los Angeles metropolitan zone localized in Southern California (USA), that is a tectonically active region with surface deformations that are a combination of fault related tectonics plus a variety of other natural and anthropogenic signals (Anderson et al., 2003; Argus et al., 2005; Fuis et al., 2001). This zone includes effects relevant to aquifers such as the Santa Ana and Pomona basins, oil related displacement at a number of locations, and motion across the NewportInglewood and San Jose faults that, as previously mentioned, have been already investigated by applying the DInSAR technology, see Argus et al. (2005), Bawden et al. (2001), Colesanti et al. (2003), Lanari et al. (2004a), and Watson et al. (2002).

A key element for the seismic surveillance on the whole area is represented by the Southern California Integrated GPS Network which is an array of GPS stations, described by Hudnut et al. (2001), that reached in the summer of 2001 its target goal of 250 operational sites spread out across southern California and northern Baja California, Mexico. The map of the area investigated in this study, which highlighted the GPS SCIGN sites, is shown in Fig. 7. For what concerns the presented DInSAR analysis, we have reconsidered the results of the study presented by Lanari et al. (2004a), that was originally focused on the Santa Ana basin aquifer. In this case the SBAS algorithm has been applied to a set of 42 SAR data acquired by the ERS satellites from late 1995 into 2002 (track: 170, frame: 2925), coupled in 102 interferograms with a perpendicular baseline smaller than 300 m and a maximum time interval of 4 years; precise satellite orbital information and an SRTM DEM of the study area have also been used. This is consistent with the DInSAR products relevant to the previously discussed analysis on the Napoli bay site (see Section 3); also in this case the results have been obtained following a complex multilook operation, with 4 looks in the range direction and 20

Fig. 7. SAR amplitude image of the investigated portion of the Los Angeles metropolitan area with the black squares marking the SCIGN GPS site locations; the GPS site labeled ELSC is highlighted. The inset in the upper right corner shows the location of the study area.

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

looks in the azimuth one, with a resulting pixel dimension of the order of 100 × 100 m. As a first result of the SBAS algorithm analysis, we present in Fig. 8 the LOS mean displacement velocity map computed via the SBAS processing, superimposed to a shaded relief of the SAR image relevant to the investigated area and which highlighted the location of the major surface deformation features. We further remark that in Fig. 8 we have identified via the black and the white squares the positions of the SCIGN GPS sites that are located in coherent areas of the DInSAR map and for which measurements are available before 2000, thus ensuring at least 2 years of overlap with the available SAR data. Following our GPS selection, an extensive comparison has been carried out between the homologous DInSAR and GPS time series, the latter obtained through the SCIGN website (http://www.scign.org/) and projected on the SAR sensor line of sight in order to be consistent with the radar observations. Moreover, all the measurements (SAR and GPS) have been computed with respect to a reference pixel located in correspondence of the ELSC station highlighted in Fig. 8. Similarly to Section 3, we have started our analysis by first providing a qualitative comparison between SAR and LOSprojected GPS displacements on a selection of GPS sites with a rather uniform distribution in the test area and characterized by different features in their deformation time series. These twelve stations are those identified by the white squares of Fig. 8 and are labeled as CLAR, CVHS, LONG, CIT1,

207

DSHS, WHC1, SNHS, SACY, CCCO, TORP, LBC2 and FVPK, respectively. For each of these stations we have compared the DInSAR results (red triangles) with the corresponding LOS-projected GPS measurements (black stars) available within the SAR acquisition window. The results of these comparisons are shown in the plots of Fig. 9, clearly showing the good agreement between these two measurements. Following these results we focused on a quantitative assessment of the SAR measurements quality. Accordingly, we have compared the DInSAR time series with the corresponding LOS-projected GPS measurements for all the sites identified in Fig. 8 by the black and white squares. In particular, we have computed the standard deviation value of the differences between these two time series; the achieved results are summarized in the central column of Table 3. Based on these measurements, we have computed the average standard deviation value relevant to the differences between SAR and GPS data which corresponds to rdG ¼ 6:9 mm. We further remark that 50% and 100% of the differences between SAR and GPS data are included within the ð−rdG ; rdG Þ and ð−2rdG ; 2rdG Þ intervals, respectively. 5. Conclusions The presented analysis clearly demonstrates the capability of the SBAS technique to detect, from ERS data set including between 40 and 60 images, displacements with mean deformation velocities of the order of 1 mm/year. Moreover,

Fig. 8. Mean LOS displacement velocity map computed in coherent areas, with respect to the investigated 1995–2002 time interval, and superimposed to a shaded relief of the SAR image relevant to the Los Angeles zone; the major surface deformation features and the GPS site ELSC, located in correspondence of the SAR reference pixel, have been highlighted. The pixels identified by (black and white) squares mark a selection of the SCIGN GPS sites that are located in coherent areas and for which measurements are available before 2000. In particular, the white squares identify the GPS sites relevant to the plots shown in the following Fig. 9.

208

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

Fig. 9. Comparison between the DInSAR LOS deformation time series (red triangles) and the GPS measurements projected on the radar LOS (black stars) for the pixels labeled in Fig. 8 as CLAR (a), CVHS (b), LONG (c), CIT1 (d), DSHS (e), WHC1 (f), SNHS (g), SACY (h), CCCO (i), TORP (j), LBC2 (k) and FVPK (l), respectively. The standard deviation value of the differences between the SAR and LOS-projected GPS measurements has been reported in the plots (a)–(l).

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

the standard deviation values of the SAR/leveling and SAR/ GPS deformation time series differences correspond to 4.7 mm and 6.9 mm respectively. Accordingly, it is evident from these results the capability of the procedure to provide deformation time series having a sub-centimetric accuracy with respect to a common reference point of known motion. Moreover, we further remark that in our analysis, we have assumed the leveling and GPS data as a reference, thus completely neglecting the errors that may affect these measurements. However, we may reconsider this issue by referring to the typical accuracy of the geometric leveling data which is about 2 mm (INGV-OV, 2003), and to the estimated LOS-projected GPS errors listed in the right hand side column of Table 3. By considering these values, we may remove the bias due to the estimated errors relevant to the geodetic measurements. Table 3 Results of the comparison between SAR and LOS-projected GPS deformation time series relevant to the Los Angeles area GPS stations

AZU1 BGIS BRAN CCCO CCCS CIT1 CLAR CRHS CSDH CVHS DSHS DYHS ECCO EWPP FVPK JPLM LASC LBC1 LBC2 LONG LORS LPHS MHMS NOPK PMHS PVHS PVRS RHCL SACY SNHS SPMS TORP USC1 VTIS VYAS WCHS WHC1 WHI1 a

Standard deviation of the difference between SAR and LOS-projected GPS measurements [cm]

LOS-projected GPS errors a [cm]

0.71 0.48 0.85 0.68 0.73 0.70 1.09 0.41 0.58 0.53 0.54 0.49 0.48 0.61 0.82 0.93 0.76 0.79 0.59 0.90 0.81 0.47 1.13 0.47 0.50 0.77 0.77 0.49 0.85 0.72 0.65 0.69 0.73 0.80 0.48 0.49 0.38 0.20

0.43 0.38 0.42 0.37 0.37 0.38 0.37 0.37 0.36 0.38 0.43 0.37 0.38 0.37 0.37 0.41 0.37 0.42 0.37 0.43 0.39 0.40 0.50 0.44 0.38 0.44 0.40 0.39 0.43 0.39 0.37 0.38 0.42 0.40 0.39 0.39 0.39 0.39

These values have been obtained by projecting along the radar line of sight the information relevant to the errors available from the SCIGN web-site (http://www.scign.org/).

209

Fig. 10. Plot of the standard deviation values (triangles) listed in the central column of Table 3 vs. the distance of each corresponding GPS station with respect to the ELSC site identified in Fig. 8. The continuous line represents the result of the linear best fit, showing that there is an increase of the standard deviation value as we move away from the reference pixel, with an estimated variation rate of about 0.05 mm/km.

Accordingly, we finally obtain from the SAR/leveling and SAR/GPS deformation time series comparisons the standard deviation values rdL ¼ 4:3 mm and rdG ¼ 5:6 mm, respectively. These results allow us to conclude that a value of σd = 5 mm represents a very realistic number for the standard deviation of the DInSAR LOS deformation time series. As a final remark, we stress that in our analysis we did not investigate the dependence of our error estimates with respect to the location of the reference SAR pixel. However, if we consider the results presented in Section 43 and plot the GPS standard deviations values computed for each GPS locations with respect to the distance from the reference SAR pixel, we obtain the results shown in the plot of Fig. 10, wherein the continuous line corresponds to the computed linear best fit. Although this analysis deserves a deeper investigation, it is quite clear from the plot of Fig. 10 that there is an increase of the standard deviation value as we move away from the reference pixel, with an estimated variation rate of about 0.05 mm/km. The main reasons justifying the error dependence on the distance from the reference SAR pixel are related to different issues. First of all there is the effect of possible residual orbital ramps. Moreover, uncompensated atmospheric artifacts and phase unwrapping errors may also contribute to this error dependence which is an additional, relevant issue to be taken into account when considering the accuracy we may expect in the deformation products retrieved via the SBAS technique. Acknowledgements This work has been partially sponsored by the European Community on Provision 3.16, under the project of the Regional Center of Competence “Analysis and Monitoring of the Environmental Risk” (CRdC-AMRA) and by the Italian Space Agency and the (Italian) National Group for Volcanology (GNV). We thank the European Space Agency, which has provided the ERS SAR data relevant to the Napoli bay area within the Cat-1 1065 and 1318 and the ERS data of the Los Angeles zone through 3 These results are considered because of the rather large and uniform distribution of the measured pixels within the investigated area, see Fig. 8; this is not the case for the results of Section 3 that are mostly concentrated along a portion of the Napoli bay coast line, see Fig. 4a.

210

F. Casu et al. / Remote Sensing of Environment 102 (2006) 195–210

the WInSAR data archive in collaboration with P. Lundgren, JPL, Caltech. The GPS measurements relevant to the SCIGN network have been obtained through the SCIGN web-site (http://www. scign.org/). Moreover, the DEMs of the investigated zones have been achieved through the SRTM archive while precise ERS-1/ ERS-2 satellite orbit state vectors are courtesy of the Technical University of Delft, The Netherlands. The authors also want to thank the Osservatorio Vesuviano which has provided the leveling measurements of the Napoli bay site and in particular G. P. Ricciardi for his continuous help and support. We finally thank the anonymous reviewers for their valuable remarks and suggestions and N. Gourmelen for correcting the manuscript. References Anderson, G., Aagaard, B., & Hudnut, K. (2003). Fault interactions and large complex earthquakes in the Los Angeles area. Science, 302(5652), 1946−1949. Argus, D. F., Heflin, M. B., Peltzer, G., Crampé, F., & Webb, F. H. (2005). Interseismic strain accumulation and anthropogenic motion in metropolitan Los Angeles. Journal of Geophysical Research, 110(B4) (Art. No. B04401). Avallone, A., Zollo, A., Briole, P., Delacourt, C., & Beauducel, F. (1999). Subsidence of Campi Flegrei (Italy) detected by SAR interferometry. Geophysical Research Letters, 26, 2303−2306. Bawden, G. W., Thatcher, W., Stein, R. S., Hudnut, K. W., & Peltzer, G. (2001). Tectonic contraction across Los Angeles after removal of groundwater pumping effects. Nature, 412, 812−815. Beauducel, F., De Natale, G., Obrizzo, F., & Pingue, F. (2004). 3-D modelling of Campi Flegrei ground deformations: Role of caldera boundary discontinuities. Pure and Applied Geophysics, 161(7), 1329−1344. Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40 (11), 2375−2383. Borgia, A., Tizzani, P., Solaro, G., Manzo, M., Casu, F., Luongo, G., et al. (2005). Volcanic spreading of Vesuvius, a new paradigm for interpreting its volcanic activity. Geophysical Research Letters, 32, L03303. doi:10.1029/ 2004GL022155 Colesanti, C., Ferretti, A., Prati, C., & Rocca, F. (2003). Monitoring landslides and tectonic motions with the Permanent Scatterers Technique. Engineering Geology, 68(1), 3−14. Costantini, M., & Rosen, P. A. (1999). A generalized phase unwrapping approach for sparse data. IGARSS'99 Proc., Hamburg (Germany) (pp. 267−269). Crosetto, M., Crippa, B., & Biescas, E. (2005). Early detection and in-depth analysis of deformation phenomena by radar interferometry. Engineering Geology, 79(1–2), 81−91. Ferretti, A., Prati, C., & Rocca, F. (2000). Non-linear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transaction on Geoscience and Remote Sensing, 38, 5. Fuis, G. S., Ryberg, T., Godfrey, N. J., Okaya, D. A., & Murphy, J. M. (2001). Crustal structure and tectonics from the Los Angeles Basin to the Mojave Desert, Southern California. Geology (Boulder), 29(1), 15−18. Gabriel, A. K., Goldstein, R. M., & Zebker, H. A. (1989). Mapping small elevation changes over large areas: Differential interferometry. Journal of Geophysical Research, 94, 9183−9191. Goldstein, R. M. (1995). Atmospheric limitations to repeat-track radar interferometry. Geophysical Research Letters, 22, 2517−2520. Hooper, A., Zebker, H., Segall, P., & Kampes, B. (2004). A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical Research Letters, 31, L23611. doi:10.1029/2004GL021737

Hudnut, K. W., Bock, Y., Galetzka, J. E., Webb, F. H., & Young, W. H. (2001). The Southern California Integrated GPS Network (SCIGN). Proceedings of the 10th FIG International Symposium on Deformation Measurements 19– 22 March 2001 (pp. 129−148). California, USA: Orange. INGV-Osservatorio Vesuviano. (2001). Rendiconto sull'attività di sorveglianza. INGV-Osservatorio Vesuviano. (2002). Rendiconto sull'attività di sorveglianza. INGV-Osservatorio Vesuviano. (2003). Rendiconto sull'attività di sorveglianza. Lanari, R., De Natale, G., Berardino, P., Sansosti, E., Ricciardi, G. P., Borgstrom, S., et al. (2002). Evidence for a peculiar style of ground deformation inferred at Vesuvius volcano. Geophysical Research Letters, 29 (9). doi:10.1029/2001GL014571 Lanari, R., Berardino, P., Borgström, S., Del Gaudio, C., De Martino, P., Fornaro, G., et al. (2004a). The use of IFSAR and classical geodetic techniques for caldera unrest episodes: Application to the Campi Flegrei uplift event of 2000. Journal of Volcanology and Geothermal Research, 133, 247−260. Lanari, R., Lundgren, P., Manzo, M., & Casu, F. (2004b). Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California. Geophysical Research Letters, 31, L23613. doi:10.1029/2004GL021294 Lanari, R., Zeni, G., Manunta, M., Guarino, S., Berardino, P., & Sansosti, E. (2004c). An integrated SAR/GIS approach for investigating urban deformation phenomena: The city of Napoli (Italy) case study. International Journal of Remote Sensing, 25, 2855−2862. Lanari, R., Mora, O., Manunta, M., Mallorquí, J. J., Berardino, P., & Sansosti, E. (2004d). A small baseline approach for investigating deformations on full resolution differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 42, 7. Lundgren, P., Usai, S., Sansosti, E., Lanari, R., Tesauro, M., Fornaro, G., et al. (2001). Modeling surface deformation observed with synthetic aperture radar interferometry at Campi Flegrei caldera. Geophysical Research Letters, 106, 19, 355–19, 366. Lundgren, P., Casu, P., Manzo, M., Pepe, A., Berardino, P., Sansosti, E., et al. (2004). Gravity and magma induced spreading of Mount Etna volcano revealed by satellite radar interferometry. Geophysical Research Letters, 31, L04602. doi:10.1029/2003GL018736 Pepe, A., Sansosti, E., Berardino, P., & Lanari, R. (2005). On the Generation of ERS/ENVISAT DInSAR Time-Series via the SBAS technique. IEEE on Geoscience and Remote Sensing Letters, 2(3), 265−269. Mora, O., Mallorquí, J. J., & Broquetas, A. (2003). Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE on Transaction Geoscience and Remote Sensing, 41, 2243−2253. Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., et al. (2000). Synthetic aperture radar interferometry. IEEE Proceedings, 88, 333−376. Rosen, P. A., Hensley, S., Gurrola, E., Rogez, F., Chan, S., & Martin, J. (2001). SRTM C-band topographic data quality assessment and calibration activities. Proc. of IGARSS'01 (pp. 739−741). SCIGN web-site — http://www.scign.org/ Tesauro, M., Berardino, P., Lanari, R., Sansosti, E., Fornaro, G., & Franceschetti, G. (2000). Urban subsidence inside the city of Napoli (Italy) observed by satellite radar interferometry. Geophysical Research Letters, 27(13), 1961−1964. Usai, S. (2003). A least squares database approach for SAR interferometric data. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 753−760. Watson, K. M., Bock, Y., & Sandwell, D. T. (2002). Satellite interferometric observations of displacements associated with seasonal groundwater in the Los Angeles basin. Journal of Geophysical Research, 107(B4). doi:10.1029/ 2001JB000470 Werner, C., Wegmuller, U., Strozzi, T., & Wiesmann, A. (2003). Interferometric point target analysis for deformation mapping. Proceedings of IGARSS '03, vol. 7. (pp. 4362−4364). Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes. IEEE Transactions on Geoscience and Remote Sensing, 30, 950−959.